Thabet et al., 2019 - Google Patents
Sample-efficient deep reinforcement learning with imaginary rollouts for human-robot interactionThabet et al., 2019
View PDF- Document ID
- 13531545060384721578
- Author
- Thabet M
- Patacchiola M
- Cangelosi A
- Publication year
- Publication venue
- 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
External Links
Snippet
Deep reinforcement learning has proven to be a great success in allowing agents to learn complex tasks. However, its application to actual robots can be prohibitively expensive. Furthermore, the unpredictability of human behavior in human-robot interaction tasks can …
- 230000003993 interaction 0 title abstract description 16
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Vecerik et al. | Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards | |
Karkus et al. | Differentiable algorithm networks for composable robot learning | |
Mandlekar et al. | Iris: Implicit reinforcement without interaction at scale for learning control from offline robot manipulation data | |
Wang et al. | Robust imitation of diverse behaviors | |
US20210390653A1 (en) | Learning robotic tasks using one or more neural networks | |
Rahmatizadeh et al. | Vision-based multi-task manipulation for inexpensive robots using end-to-end learning from demonstration | |
Thabet et al. | Sample-efficient deep reinforcement learning with imaginary rollouts for human-robot interaction | |
Ding et al. | Challenges of reinforcement learning | |
Ren et al. | Generalization guarantees for imitation learning | |
Cuccu et al. | Intrinsically motivated neuroevolution for vision-based reinforcement learning | |
Hu et al. | On Transforming Reinforcement Learning With Transformers: The Development Trajectory | |
CN113379027A (en) | Method, system, storage medium and application for generating confrontation interactive simulation learning | |
Sakunthala et al. | A review on artificial intelligence techniques in electrical drives: Neural networks, fuzzy logic, and genetic algorithm | |
Hafez et al. | Efficient intrinsically motivated robotic grasping with learning-adaptive imagination in latent space | |
Tanwani | Generative models for learning robot manipulation skills from humans | |
Seo et al. | Continuous control with coarse-to-fine reinforcement learning | |
JP2021192141A (en) | Learning device, learning method, and learning program | |
Dinerstein et al. | Learning policies for embodied virtual agents through demonstration | |
Kobayashi et al. | Latent representation in human–robot interaction with explicit consideration of periodic dynamics | |
Przystupa et al. | Deep probabilistic movement primitives with a bayesian aggregator | |
Chien et al. | Stochastic curiosity maximizing exploration | |
Lötzsch | Using deep reinforcement learning for the continuous control of robotic arms | |
Li et al. | A hierarchical reinforcement learning method for persistent time-sensitive tasks | |
Pong | Goal-Directed Exploration and Skill Reuse | |
Chen et al. | Imitating shortest paths for visual navigation with trajectory-aware deep reinforcement learning |